Burst-induced Multi-Armed Bandit for learning recommendation

Burst-induced Multi-Armed Bandit for learning recommendation

In this paper, we introduce a non-stationary and context-free Multi-Armed Bandit (MAB) problem and a novel algorithm (which we refer to as BMAB) to solve it. The problem is context-free in the sense that no side information about users or items is needed. We work in a continuous-time setting where e...

Full description

Saved in:
Bibliographic Details
Main Authors: ALVES, Rodrigo, LEDENT, Antoine, KLOFT, Marius
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2021
Subjects:
Recommender Systems
Reinforcement Learning
Online learning
Poisson processes
Time Series Analysis
bursty methods
audience dynamics
Artificial Intelligence and Robotics
Numerical Analysis and Scientific Computing
Online Access:https://ink.library.smu.edu.sg/sis_research/7209
https://ink.library.smu.edu.sg/context/sis_research/article/8212/viewcontent/3460231.3474250.pdf
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Singapore Management University
Language: English

Internet

https://ink.library.smu.edu.sg/sis_research/7209
https://ink.library.smu.edu.sg/context/sis_research/article/8212/viewcontent/3460231.3474250.pdf

Similar Items